While data-driven and model-driven approaches can share knowledge learned on different domains, such models are still trained on a pre-defined set of labels, and can not handle unseen labels, i.e. not mapped to the existing schema. For example, a model trained to recognize a Destination slot, can not be used directly to recognize the slot Arrival_Location for a new domain, although both slots are semantically similar. For this reason, researchers have recently been working on zero-shot models, trained on label representations that leverage natural language descriptions of the slots [1]}, [2]}. Assuming that accurate slot descriptions are provided, slots with different names although semantically similar would have similar description as well. Thus, having trained a model for the Destination slot with its descriptions, it is now possible to recognize the slot Arrival_Location without training on it, but only supplying the corresponding slot description.
